Apologies if this isn't the right place to ask. But I'm currently studying point cloud-based networks like pointcloud++, and all the related 3d object detection networks like pointpillars, voxelnet, etc. While I (think) understand the algorithms like feature propagation in pointnet++. I'm having trouble understanding how would one implement them. Or Where could I learn about writing operations in cuda and making sure they are compatible with backprop?
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We train the Deep reinforcement Learning model for IoT devices/Unmanned aerial vehicles at GPU and we have enough resources to train over there, what if we have to train that model on IoTs/UAVs, is it possible for UAV to compute that model?
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In this article, let’s discuss one of the trendy and handy web-scraping tools, Octoparse, and its key features and how to use it for our data-driven solutions. Hope you all are familiar with “WEB SCRAPING” techniques, and the captured data has been used to analyze business perceptions further. If you look at the end-end process… Read More »Exploring Octoparse for Data Preparations and Product Assessment
The post Exploring Octoparse for Data Preparations and Product Assessment appeared first on Data Science Central.
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Batch indexing into multidimensional tensors/arrays is kind of tricky, I made this project explaining the builtin syntax and also made wrappers for simplifying the interface, with additional features for underlying coordinate grid data (like signed distance functions) that need to be indexed by coordinate value rather than integer indices directly https://github.com/LemonPi/multidim_indexing
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Google published results from an seq2seq transformer model for autoregressive image generation.
Website: https://parti.research.google/
Paper: https://gweb-research-parti.web.app/parti_paper.pdf
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We are researchers at Carnegie Mellon University studying how software developers identify and act on ethical concerns at work. If you’re interested in helping us advance research in software ethics, please fill out this survey and we’ll reach out to you for a quick interview!
P.S.
You can check out this Stack Overflow blog post to read more about the direction of our research.
Anything you disclose to us during the survey / interview may appear in our study but will not be traceable to you.
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According to Gartner, hyperautomation is the number one trend in 2022 and will continue advancing in future. One of the main barriers to hyperautomation is in areas where we’re still struggling to reduce human involvement. Intelligent systems have a hard time matching human visual recognition abilities, despite great advancements in deep learning in computer vision. […]
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Deep learning models with billions of parameters are trained through gradient-based stochastic optimization, thanks to powerful algorithms, systems, and hardware advancements. These algorithms include several hyperparameters that are essential for effective performance. Hyperparameter adjustment is required to control the behavior of a machine learning model. If our hyperparameters are not correctly set, our anticipated model parameters will not minimize the loss function, resulting in poor results. The lousy result suggests that our model has further faults. In actuality, the accuracy or confusion matrix will be worse.
Many hyperparameters exist like learning rate, regularisation type, degree, and size of neural network layers. Automating the setting of these hyperparameters and accelerating the training of neural network weights are necessary if domain experts and industry practitioners benefit from the most recent deep learning technologies. Even for specialists, tuning them takes a lot of time and effort; choosing the best hyperparameter configuration frequently depends on factors like cost or latency.
Continue reading | Checkout the paper, github
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A camera begins in the sky, flies through some trees and smoothly exits the forest, all while precisely tracking a car driving down a dirt path. This would be all but impossible in the real world, according to film and photography director Brett Danton.
The post Meet the Omnivore: Director of Photography Revs Up NVIDIA Omniverse to Create Sleek Car Demo appeared first on NVIDIA Blog.
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It may seem intuitive that AI and deep learning can speed up workflows — including novel drug discovery, a typically years-long and several-billion-dollar endeavor. But professors Artem Cherkasov and Olexandr Isayev were surprised to find that no recent academic papers provided a comprehensive, global research review of how deep learning and GPU-accelerated computing impact drug Read article >
The post Artem Cherkasov and Olexandr Isayev on Democratizing Drug Discovery With NVIDIA GPUs appeared first on NVIDIA Blog.
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Robotics Process Automation (RPA) is all about incorporating solutions that handle repetitive tasks faster and more efficiently. These…
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Specialization Intro video: https://youtu.be/g7dv-Lnuor4
Specialization on Coursera: https://www.coursera.org/specializations/machine-learning-introduction
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Hi
So we can finally play around with the cool NVLabs EG3D, but they refuse to release the inversion script.
Does anyone have success to pass a image and reconstruct a face in this project?
I am not having success when trying to do this, so I would greatly appreciate if anyone could share how to do it or if you know of an existing fork?
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Amazon Web Services and Udacity are partnering to offer free services to educate developers of all skill levels on machine learning (ML) concepts with the AWS Machine Learning Engineer Scholarship program. The program offers free enrollment to the AWS Machine Learning Foundations course and 325 scholarships awarded to the AWS Machine Learning Engineer Nanodegree, a […]
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Mangrove forests are an import part of a healthy ecosystem, and human activities are one of the major reasons for their gradual disappearance from coastlines around the world. Using a machine learning (ML) model to identify mangrove regions from a satellite image gives researchers an effective way to monitor the size of the forests over […]
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The increasing ubiquity of satellite data over the last two decades is helping scientists observe and monitor the health of our constantly changing planet. By tracking specific regions of the Earth’s surface, scientists can observe how regions like forests, water bodies, or glaciers change over time. One such region of interest for geologists is mangrove […]
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Early last year, our research team from the Visual Computing Group introduced Swin Transformer, a Transformer-based general-purpose computer vision architecture that for the first time beat convolutional neural networks on the important vision benchmark of COCO object detection and did so by a large margin. Convolutional neural networks (CNNs) have long been the architecture of […]
The post Swin Transformer supports 3-billion-parameter vision models that can train with higher-resolution images for greater task applicability appeared first on Microsoft Research.
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Google JAX is a powerful framework for machine learning that offers many benefits over other popular frameworks such as PyTorch and…
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Hello, i wanna create neural network that will read DMG dealt fields and output them from picture like this. So far i have 1677 of them (they are mostly 3 field but some have 2 or 1). Do you think its enough to label or should i gather more?
And one more question is if its good idea to try to train it on these pictures or should i split pictures so they are individual field of dmg dealt?
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MIT scientists unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles.
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Jazz is all about improvisation — and NVIDIA is paying tribute to the genre with AI research that could one day enable graphics creators to improvise with 3D objects created in the time it takes to hold a jam session. The method, NVIDIA 3D MoMa, could empower architects, designers, concept artists and game developers to Read article >
The post AI in the Big Easy: NVIDIA Research Lets Content Creators Improvise With 3D Objects appeared first on NVIDIA Blog.
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The metaverse is the next big step in the evolution of the internet — the 3D web — which presents a major opportunity for every industry from entertainment to automotive to manufacturing, robotics and beyond. That’s why NVIDIA is joining our partners in the Metaverse Standards Forum, an open venue for all interested parties to Read article >
The post NVIDIA Joins Forum to Help Lay the Foundation of the Metaverse appeared first on NVIDIA Blog.
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3D artist Jae Solina, who goes by the stage name JSFILMZ, steps In the NVIDIA Studio this week to share his unique 3D creative workflow in the making of Cyberpunk Short Film — a story shrouded in mystery with a tense exchange between two secretive contacts.
The post 3D Artist Jae Solina Goes Cyberpunk This Week ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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Salesforce has built an open-source machine learning framework called OmniXAI, which stands for Omni eXplainable AI. This library takes an “omni-directional” approach to XAI, with extensive interpretable ML features that address many problems with explaining ML model decisions in reality. OmniXAI is a one-stop comprehensive library that makes explainable AI accessible to academics requiring explanations for each stage of the machine learning process. This is not limited to data exploration, feature engineering, model development, evaluation, decision making, etc.
🚦 A one-stop solution for analyzing different stages in a standard ML pipeline in real-world applications.
🚦 Two types of explanations — local and global
🚦 Includes most popular explanation methods, such as feature-attribution/importance explanation (LIME [1], SHAP [2], Integrated Gradients (IG) [3], Grad-CAM [4], L2X), counterfactual explanation (MACE [5]), partial dependence plots (PDP), and model-specific methods (linear and tree models)
🚦 Can be applied on tabular, vision, NLP, and time-series tasks.
Continue reading | Checkout the paper, article, github, dashboard
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Salesforce has built an open-source machine learning framework called OmniXAI, which stands for Omni eXplainable AI. This library takes an “omni-directional” approach to XAI, with extensive interpretable ML features that address many problems with explaining ML model decisions in reality. OmniXAI is a one-stop comprehensive library that makes explainable AI accessible to academics requiring explanations for each stage of the machine learning process. This is not limited to data exploration, feature engineering, model development, evaluation, decision making, etc.
🚦 A one-stop solution for analyzing different stages in a standard ML pipeline in real-world applications.
🚦 Two types of explanations — local and global
🚦 Includes most popular explanation methods, such as feature-attribution/importance explanation (LIME [1], SHAP [2], Integrated Gradients (IG) [3], Grad-CAM [4], L2X), counterfactual explanation (MACE [5]), partial dependence plots (PDP), and model-specific methods (linear and tree models)
🚦 Can be applied on tabular, vision, NLP, and time-series tasks.
Continue reading | Checkout the paper, article, github, dashboard
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Changes the page CSS and text editor and generates Python code to change Matplotlib styles to match the theme the user choses. Users may import themes or use any of the 50+ provided. Colab Themes enhances the data science experience by transforming the way users view their code and their data!
Check it out on Github or install it via the Chrome Webstore
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https://preview.redd.it/efbfdsbhmo691.png?width=964&format=png&auto=webp&s=4593b345d28e393447c4cf66af2abdbca72309c9
Everywhere that I have read, Policy-Based methods are supposed to be more robust and converge faster than Value-Based methods.
Why does this table contradict that?
Edit:
Link to image: Atari games Benchmark (Atari Games) | Papers With Code
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This blog post is co-written with Nick Vargas and Anna Schreiber from Accenture. Scheduling customer appointments is often a manual and labor-intensive process. You can utilize advances in self-service technology to automate appointment scheduling. In this blog post, we show you how to build a self-service appointment scheduling solution built with Amazon Lex and Amazon […]
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If the King of Sweden wants help drafting his annual Christmas speech this year, he could ask the same AI model that’s available to his 10 million subjects. As a test, researchers prompted the model, called GPT-SW3, to draft one of the royal messages, and it did a pretty good job, according to Magnus Sahlgren, Read article >
The post The King’s Swedish: AI Rewrites the Book in Scandinavia appeared first on NVIDIA Blog.
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https://www.businessinsider.com/facebook-pytorch-beat-google-tensorflow-jax-meta-ai-2022-6
With companies and researchers leaving Tensorflow and going to PyTorch, Google seems to be interested in moving its products to JAX, addressing some pain points from Tensorflow like the complexity of API, and complexity to train in custom chips like TPU. The article says that JAX still has long way to go since it lacks proper optimization to GPUs and CPUs when compared to TPUs.
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This guy seems to be having a company that teaches AI-ethics to industry elites in Sweden.
https://pbs.twimg.com/profile_images/1231981924085882880/iM_9ACFb_400x400.jpg
He is also a plagiarist: https://andreasplagiarism.wordpress.com/2020/12/02/andreas-theodorou-committed-plagiarism-in-his-phd-thesis/
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🚦 HIPT is pretrained across 33 cancer types using 10,678 gigapixel WSIs, 408,218 4096×4096 images, and 104M 256 × 256 images
🚦 HIPT pushes the boundaries of both Vision Transformers and self-supervised learning in two important ways.
🚦 The code is available
Continue reading | Checkout the paper, github
https://i.redd.it/5jt6a83deg691.gif
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Defined as:
"a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher."
Keep in mind that a passive progress meter or a proficiency model does not qualify as an ITS.
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This Ai ethicist is a highly funded academic plagiarist.
https://andreasplagiarism.wordpress.com/2020/12/02/andreas-theodorou-committed-plagiarism-in-his-phd-thesis/
Despite this he is kept in academia.
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https://bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-022-04048-1
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I once worked with a researcher, she wanted to collect some Reddit data related to a particular topic, and wanted to train a machine learning model with it. I realised how difficult it is for non-programmers to get into building machine learning models for such use cases, so I decided to shape the project myself, and I open sourced it.
Supports:
Text Data
Image Data
The project does everything in just two steps.Execution is as simple as this:
Make a config file with your required details of input.
Run the API in a single line with the config passed as input.
Here's the link to the project: https://github.com/nfflow/redditflow/
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After 8 months of long coding nights ☕ we finally officially release Pythae 🥳, a python library unifying generative autoencoder implementations including vaegan🥗, vqvae or RAEs. I hope you will enjoy it!
🖥️ github repo: https://github.com/clementchadebec/benchmark_VAE
👉paper: https://arxiv.org/abs/2206.08309
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I'm sure you all saw the recent news about a Google employee suggesting their LaMDA AI was sentient (based on conversational exchanges like these). Experts have generally dismissed this claim, and rightly so. Conversational AI systems are designed to use language in a way that sounds human, whereas our human brains select linguistic responses to solve much more complex problems, with objectives such as meeting our physical or emotional needs.
Still, I think it's interesting to ask how one could demonstrate, by testing only verbal responses to verbal input (rather than examining its code or hardware) that such conversational AIs aren't sentient -- and in particular, whether such a test can be made robust against future improvements to the system. That is, generic future improvements to th…
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SpaceRobotEnv is an open-sourced environments for trajectory planning of free-floating space robots. Reaching high-level planning accuracy, bimanual coordination and end-to-end control remains an open challenge for space robotics researchers. To better help the community study this problem, SpaceRobotEnv are developed with the following key features: Real Space Environment; Dynamic coupling control; Image input. URL: https://github.com/Tsinghua-Space-Robot-Learning-Group/SpaceRobotEnv
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A new technique in computer vision may enhance our three-dimensional understanding of two-dimensional images.
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Hey everyone!
At the end of last year, I have submitted my Master's Thesis at TU Berlin, a report about the implementation and evaluation of an expressive Variational Autoencoder augmentation of the Tacotron Text-To-Speech System, called Capacitron from the Google team.
With some help from the awesome Coqui TTS community, we have managed to build the prosody encoder VAE module in a modular way, so that this prosodic augmentation can be also implemented with Tacotron 2 - this is a massive improvement in stability and quality compared to the original method, where the authors worked with a Tacotron 1 based architecture.
I have written a short technical summary/blog post about some implementation details and audio examples on Medium.
If you'd like to try out the model, you can do so in this colab.
For the full thesis, follow this link.
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Hi, I just wanted to share that I've released the version 0.9.0 of sklearn-genetic-opt, the main change includes the option to use adaptive parameters to explore the space of hyperparameters during tuning, this has the advantage of being able to explore larger regions at the first iterations and keep the best ones at the end.
You can learn more about it here, any suggestion or contribution is welcome :)
https://preview.redd.it/unrw6dtsxz591.png?width=640&format=png&auto=webp&s=a59c91d6560806fdf1b12c24faee6aad38d75c26
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This post is co-written with Sowmya Manusani, Sr. Staff Machine Learning Engineer at Zendesk Zendesk is a SaaS company that builds support, sales, and customer engagement software for everyone, with simplicity as the foundation. It thrives on making over 170,000 companies worldwide serve their hundreds of millions of customers efficiently. The Machine Learning team at […]
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Accounting for nearly half of global vehicle sales in 2021, SUVs have grown in popularity given their versatility. Now, NIO aims to amp up the volume further. This week, the electric automaker unveiled the ES7 SUV, purpose-built for the intelligent vehicle era. Its sporty yet elegant body houses an array of cutting-edge technology, including the Read article >
The post Smart Utility Vehicle: NIO ES7 Redefines Category with Intelligent, Versatile EV Powered by NVIDIA DRIVE Orin appeared first on NVIDIA Blog.
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At a time when much about COVID-19 remained a mystery, U.K.-based PrecisionLife used AI and combinatorial analytics to discover new genes associated with severe symptoms and hospitalizations for patients. The techbio company’s study, published in June 2020, pinpoints 68 novel genes associated with individuals who experienced severe disease from the virus. Over 70 percent of Read article >
The post AI for Personalized Health: Startup Advances Precision Medicine for COVID-19, Chronic Diseases appeared first on NVIDIA Blog.
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A new computational model could explain differences in recognizing facial emotions.
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AI Weirdness: the strange side of machine learning
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I was wondering if anyone knows of a gym like framework for combinaotrial optimization with reinforcement learning, which deal with max-cut, travelling sales person problem and other interesting problems on graphs, I have found one framework here https://github.com/wz26/OpenGraphGym but they do not have a gym interface, which makes it difficult for me to use standard rl libraries like RayRL or Stable baselines.
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Mantium is a global cloud platform provider for building AI applications and managing them at scale. Mantium’s end-to-end development platform enables enterprises and businesses of all sizes to build AI applications and automation faster and easier than what has been traditionally possible. With Mantium, technical and non-technical teams can prototype, develop, test, and deploy AI […]
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Amazon SageMaker Data Wrangler is a purpose-built data aggregation and preparation tool for machine learning (ML). It allows you to use a visual interface to access data and perform exploratory data analysis (EDA) and feature engineering. The EDA feature comes with built-in data analysis capabilities for charts (such as scatter plot or histogram) and time-saving […]
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Customers in industries like consumer packaged goods, manufacturing, and retail are always looking for ways to empower their operational processes by enriching them with insights and analytics generated from data. Tasks like sales forecasting directly affect operations such as raw material planning, procurement, manufacturing, distribution, and inbound/outbound logistics, and it can have many levels of […]
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Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. Edge computing is the act of running workloads on these edge devices. Machine learning at the edge (ML@Edge) is a concept that brings the […]
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In this post, we show you how to implement one of the most downloaded Hugging Face pre-trained models used for text summarization, DistilBART-CNN-12-6, within a Jupyter notebook using Amazon SageMaker and the SageMaker Hugging Face Inference Toolkit. Based on the steps shown in this post, you can try summarizing text from the WikiText-2 dataset managed […]
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Unstructured data continues to grow in many organizations, making it a challenge for users to get the information they need. Amazon Kendra is a highly accurate, intelligent search service powered by machine learning (ML). Amazon Kendra uses deep learning and reading comprehension to deliver precise answers, and returns a list of ranked documents that match […]
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Hi folks, the last epoch of the webinar series on Automated CV Pipelines is here. The previous 5 epochs covered a number of differenet ways to scale annotation projects and automate the processes. This session will sum up the key points from the previous sessions. Check it out if you are interested! I left the hyperlink in the first sentence.
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Repo link: https://github.com/hristo-vrigazov/mmap.ninja
Images Colab notebook: https://colab.research.google.com/drive/1-WMtVyfxx2aUMeV7vlG48Ia27-5cxnrS?usp=sharing
Texts Colab notebook: https://colab.research.google.com/drive/18bEwylFwx4owMpb-RAkJZS_9JrrUcFd7?usp=sharing
Hello everyone, I wrote a small, but very useful library for my personal projects and decided to share it with the world.
It deals with filesystem I/O during machine learning training. A large portion of the time spent training (especially if GPU is available) is spent on reading/writing images from the disk (or text for that matter).
For example, take the COCO 2017 validation dataset of images (I just had this one available on my machine, nothing special about it). If you can't load it all into memory at once (whic…
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For younger generations, paper bills, loan forms and even cash might as well be in a museum. Smartphones in hand, their financial services largely take place online. The financial-technology companies that serve them are in a race to develop AI that can make sense of the vast amount of data the companies collect — both Read article >
The post All-In-One Financial Services? Vietnam’s MoMo Has a Super-App for That appeared first on NVIDIA Blog.
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Google AI, or artificial intelligence, is a field of computer science that deals with the creation of intelligent machines. AI applications…
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We are excited to announce that you can now add filters to alerts and also edit existing alerts while using Amazon Lookout for Metrics. With this launch, you can add filters to your alerts configuration to only get notifications for anomalies that matter the most to you. You can also modify existing alerts as per […]
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Agility and security have historically been two aspects of IT of paramount importance for any company. With the simplification of access to advanced IT technologies thanks to low-code and no-code (LCNC) tools, an even bigger number of people must be enabled to access resources, without impacting security. For many companies, the solution has been to […]
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IT has evolved in recent years: thanks to low-code and no-code (LCNC) technologies, an increasing number of people with varying backgrounds require access to tools and platforms that were previously a prerogative to more tech-savvy individuals in the company, such as engineers or developers. Out of those LCNC technologies, we have recently announced Amazon SageMaker […]
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JIDU Auto sees a brilliant future ahead for intelligent electric vehicles. The EV startup, backed by tech titan Baidu, took the wraps off the Robo-01 concept vehicle last week during its virtual ROBODAY event. The robot-inspired, software-defined vehicle features cutting-edge AI capabilities powered by the high-performance NVIDIA DRIVE Orin compute platform. The sleek compact SUV Read article >
The post A Breakthrough Preview: JIDU Auto Debuts Intelligent Robo-01 Concept Vehicle, Powered by NVIDIA DRIVE Orin appeared first on NVIDIA Blog.
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Gal Dalal wants to ease the commute for those who work from home — or the office. The senior research scientist at NVIDIA, who is part of a 10-person lab in Israel, is using AI to reduce congestion on computer networks. For laptop jockeys, a spinning circle of death — or worse, a frozen cursor Read article >
The post The Data Center’s Traffic Cop: AI Clears Digital Gridlock appeared first on NVIDIA Blog.
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3D environment artist Jacinta Vu joins us In the NVIDIA Studio this week, showcasing her video game inspired scene Royal Library and 3D content creation workflow. Based in Cincinnati, Vu specializes in transforming 2D concept art into 3D models and scenes, a critical contribution she made to The Dragon Prince from Wonderstorm Games.
The post 3D Environment Artist Jacinta Vu Sets the Scene ‘In the NVIDIA Studio’ appeared first on NVIDIA Blog.
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The blockchain is undoubtedly the gifted invention that has sprung into something greater, created by Satoshi Nakamoto. Over the last few months, the sudden rise of this ingenious technology has been so impressive and the initial phase has already shown a great ability to rule the domain of the marketing technology landscape. Let’s take a… Read More »Blockchain Technology’s World: A Wave of Technological Progress
The post Blockchain Technology’s World: A Wave of Technological Progress appeared first on Data Science Central.
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The Internet of Things (IoT) is all about devices communicating with one another and gathering massive amounts of data to serve larger man-made aims and targets without the requirement for direct human engagement. The procedures involved in the delivery and verification, setting, retaining, tracking, and diagnosing of connected devices running as part of an IoT… Read More »IoT Device Management- Unlocking the Future with Advanced Connected Devices
The post IoT Device Management- Unlocking the Future with Advanced Connected Devices appeared first on Data Science Central.
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More than ever, earning and maintaining trust with consumers has become a mandatory part of business. To do this, companies must become obsessed with trust and privacy. Failure to do this will land your business in the news across top-tier media outlets– and for the wrong reasons! In today’s extreme information age, personal data seems… Read More »Winning Your Business, and Your Customers, with a Privacy-Led Approach
The post Winning Your Business, and Your Customers, with a Privacy-Led Approach appeared first on Data Science Central.
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Based on similar works CLIPActor and AvatarCLIP the codebase implements similar pipeline using mesh and differentiable rasterization to provide a speedup allowing for ~10 min character generation on a weak Google Colab GPU
https://twitter.com/multimodalart/status/1536608371570245632?s=20&t=Av8hJr43cvCF_HpJIz8J8g
Link to code: Github
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We just open-sourced the alpha version of our data cleaning tool: https://github.com/mage-ai/mage-ai
Looking for beta testers who would be willing to test and provide feedback!
Please send me any questions/feedback or reply here.
Demo video: https://youtu.be/cRib1zOaqWs
Thanks for the consideration!
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Hi, I made a small library that tries to generalize Pool(n_cores).map(seq, fn) in python multiprocessing stdlib. The main idea is to have a generalization Pool(n_resources).map(seq, fn) where n_resources can be any sort of resource (i.e. torch.device) and seq can be any sort of sequence (i.e. nn.Modules).
https://gitlab.com/mihaicristianpirvu/pool-resources
Here's a small example to train n > m mnist networks on m devices
python main_mnist.py
Currently, it only supports torch devices (via pool_resources.TorchDevice(x: tr.device)), however I plan to expand it to cores (start new processes) if anything else comes to mind (for example, how would i put two different keras networks on two gpus in the same process?)
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Hi everyone,
I recently went through a post on social media by a university senior of mine asking people to bring to light a case of strong plagiarism from a paper published by his group [link] and this ICCV 2021 paper, which is further corroborated by this post written by a member of his group and the co-author of the ACT paper.
There is the possibility that the authors of the former weren't aware of said publication but denial of the similarity of the two papers and still claiming to have novelty in their CVPR 2021 rebuttal (ultimately rejected.. serves them right!), and publishing the same paper without any changes at another top venue is quite toxic indeed.
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You’ve probably been told to standardize or normalize inputs to your model to improve performance. But what is normalization and how can we implement it easily in our deep learning models to improve performance? Normalizing our inputs aims to create a set of features that are on the same scale as each other, which we’ll […]
The post Using Normalization Layers to Improve Deep Learning Models appeared first on Machine Learning Mastery.
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The connection between augmented reality and test automation might not seem to have much in common at first glance, but the fact is that…
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⚡️ The largest open-source pretrained transformer for text-to-video generation in the general domain
⚡️ The first attempt to efficiently leverage the pretrained text-to-image generative model to the text-to-video generation model without hurting its image generation capacity
⚡️ CogVideo can generate high-resolution (480×480) videos
Continue reading the full summary | Check out the paper, and github
https://reddit.com/link/vbp12x/video/3ozqpjwyyg591/player
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⚡️ It can represent videos in arbitrary spatial and temporal resolution, which brings natural advantages for solving Space-Time Video SuperResolution (STVSR) tasks.
⚡️ The researchers used their experiments’ datasets from Vid4, GoPro, and Adobe240. Their findings reveal that, in addition to extrapolating out-of-distribution frame rates and spatial resolutions, VideoINR can represent video in arbitrary space and time resolutions on the scales within the training distributions.
⚡️ On in-distribution spatial and temporal scales, VideoINR performs competitively with state-of-the-art STVSR approaches and greatly outperforms other methods on out-of-distribution scales.
Continue reading | Check out the paper, github, and project
https://reddit.com/link/vbgnoq/video/0lzd2xnv4f591/player
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I was playing with the CIFAR10 dataset based on the baseline code of https://github.com/kuangliu/pytorch-cifar, but I was surprised to see a strangely large decrease in the validation performance from using a smaller learning rate.
All the experiments below use
ResNet18 model with CIFAR10 head
SGD with momentum=0.9
4-pixel random translation/horizontal flip as data augmentation
training for 200 epochs with cosine annealing to 0.
More detail can be found in https://github.com/kuangliu/pytorch-cifar or the actual personal repo used for running experiments.
The only difference with the original code is that 1) drop-out of p=0.2 is added and 2) batch size and learning rate. Note that the original code uses batch_size=128 and lr=0.1 by default and achieves 93.02% accuracy.
In t…
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As the title suggests, I am putting out a call for anyone who has a copy of the FFHQ dataset who would be able to allow me to download it from them so it can be hosted properly and made truly public.
The FFHQ dataset https://github.com/NVlabs/ffhq-dataset is a high quality, high resolution, and extremely well curated dataset that is used in many recent SOTA GAN papers and also has applications in many other areas.
FFHQ is 70k aligned images of human faces organized into a 128x128 thumbnails dataset (3GB), a 1024x1024 high res dataset (90GB), and a raw unaligned wilds dataset (900GB).
Do you have the 1024 or Wilds dataset in an s3 bucket? On Google Cloud Storage buckets? Exposed on a Globus endpoint? Kicking around on a lowly SFTP server?
Can you safely expose a share for me to downlo…
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I have made an animated video (youtu.be/p-ZCcUWzriE) for our CVPR 2022 paper (https://arxiv.org/pdf/2204.13845.pdf).
Check it out if you are interested. I have made the video using 3b1b's manim library (https://github.com/ManimCommunity/manim).
Feedback is always very welcome!
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Showing model-generated critical comments to humans helps them find flaws in summaries.
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The increasing size of language models has been one of the biggest trends in natural language processing (NLP) in recent years. Since 2018, we’ve seen unprecedented development and deployment of ever-larger language models, including BERT and its variants, GPT-2, T-NLG, and GPT-3 (175 billion parameters). These models have pushed the boundaries of possible architectural innovations. […]
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We are excited to launch a causal contribution analysis capability in Amazon Lookout for Metrics that helps you to understand the potential root causes for the business-critical anomalies in the data. Previously, you were only given the root causes for a single anomaly per measure. You had to analyze to determine if causal relationships existed […]
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ICLR (International Conference on Learning Representations) is recognized as one of the top conferences in the field of deep learning. Many influential papers on artificial intelligence, statistics, and data science—as well as important application fields such as machine vision, speech recognition, and text understanding—have been published and presented at this conference. The following selection of […]
The post ICLR 2022 highlights from Microsoft Research Asia: Expanding the horizon of machine learning techniques and applications appeared first on Microsoft Research.
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The new design is stackable and reconfigurable, for swapping out and building on existing sensors and neural network processors.
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There is no instruction to a decision making process. However, important decisions are usually made by analyzing tons of data to find the…
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In Part 1 of the blog series on building a value-driven data strategy, I discussed the challenges associated with framing the data strategy process as a deliverable. A Data Strategy, like a Business Strategy, should ebb and flow depending upon what is “valuable” to the organization given the current business environment. Instead of thinking of… Read More »Building Value-driven Data Strategy: Use Case Approach – Part 2
The post Building Value-driven Data Strategy: Use Case Approach – Part 2 appeared first on Data Science Central.
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Traveling the scenic route between Wantage, a small town in Oxfordshire, and Coventry in the U.K. meanders up steep hills, past the birthplace of Shakespeare and skirts around 19th-century English bathhouses. A project using edge computing and the world’s first 5G-enabled VR technology is enabling two engineering teams in those locales, about 70 miles apart, Read article >
The post Powered Up: 5G and VR Accelerate Vehicle Battery Design appeared first on NVIDIA Blog.
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👉 A privacy-preserving user-item graph extension protocol to expand local graphs and convey high-order information while maintaining privacy 🔒
👉 FedPerGNN yields 📉 4.0% – 9.6% reduced errors than state-of-the-art federated customization algorithms under adequate privacy protection, according to experimental results on six datasets for personalization in diverse circumstances.
👉 Furthermore, this method is not restricted to the customization scenario. It may be used as a fundamental strategy for privacy-preserving data mining on decentralized graph data, thus facilitating research in various domains involving graph-structured data.
Continue reading | Check out the paper and github
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Hey r/MachineLearning! Just wanted to share a cool utility we've built.
If you ever had real-time models running in production, and you tried to store their predictions in a Parquet file for future investigation - you know it's not such a trivial task as you'd expect. Especially if you have large amounts of inferences.
InferenceDB makes it super easy to store all your features and predictions in a Parquet file on S3. Check it out, and star the project if you like it:
https://github.com/aporia-ai/inferencedb
Would love your feedback!
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When I was doing RL with the standard open-ai gyms I felt, that these libraries are superior but cannot be transferred easily to real world problems. I was thinking which domain I would be interested in and then decided to make my own car game. Please check to code here: https://github.com/MatthiasSchinzel/Simple-Car-Game-For-Reinforcement-Learning I then trained a soft actor critic to play the game: https://github.com/MatthiasSchinzel/Soft-Actor-Critic-For-Simple-Car-Game And then used that to let SAC steer a car in GTA 5: https://github.com/MatthiasSchinzel/Soft-Actor-Critic-Playing-GTA I hope that also other users in this area might find this car game useful, even though it is still at a early stage. With the GTA 5 implementation I want to show a proof of concept, that the trained reinforcement learning algorithm can be generalized to something more realistic. Thanks for checking out the repos!
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I am doing research in NLP with the toolkit jiant (https://github.com/nyu-mll/jiant). It is a quite nice and easy-to-use tool. Unfortunately, it stopped being maintained. I wonder is there any other recommendation that I can use to replace it?
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Sequence-to-sequence (seq2seq) models that have already been trained, like BART and T5, have done very well in various natural language processing tasks, like text summarization, machine translation, answering questions, and extracting information. But these large-scale language models that have already been trained have hundreds of millions of parameters—work done at AWS AI Labs during an internship. Equal contribution trained a BART model with 400 million parameters, while T5 pushed the limit to 11 billion parameters.
👉 Empirical results show that, despite the difficult nature of language generation tasks, the research team achieves a 16.5x model footprint compression ratio with little performance drop on three generative benchmarks and further presented the performance-efficiency trade-off for seq2seq models up to a 27.7x compression ratio.
Continue reading | Check out the paper and post
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https://www.youtube.com/watch?v=WnzlbyTZsQY
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AWS offers a broad set of artificial intelligence (AI) and machine learning (ML) services, including a suite of pre-trained, ready-to-use services for developers with no prior ML experience. In this post, we demonstrate how to use such services to build an application that fosters the inclusion of people with a visual or communication impairment, which […]
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If you want to become a Web-Developer, Machine Learning and Deep Learning Engineer, Data Scientist, DevOps Engineer, and more using Python…
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AI is going to change a lot of things you can imagine.
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NVIDIA is collaborating with clinical organizations across Europe to bring AI to the point of care, bolstering clinical pathways with efficiency gains and new data dimensions that can be included in medical decision-making processes. The University Hospital Essen, in northwestern Germany, is one such organization taking machine learning from the bits to the bedside — Read article >
The post From Code to Clinic, Smart Hospital Tech Boosts Efficiency, Sustainability in Medicine appeared first on NVIDIA Blog.
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👉 Using a deep neural network of optical waveguides, a new chip developed by Penn engineers—smaller than a square centimeter—can detect and classify an image in less than a nanosecond, all without the need for a separate processor or memory unit.
👉 They have achieved this through direct processing of light received from the object of interest using an optical deep neural network implemented on a 9.3 square millimeter chip
The study published in Nature explains how the chip’s many optical neurons are linked together using optical wires or “waveguides” to construct a deep network of many “neuron layers” that resembles the human brain. Information flows across the network’s layers, with each step assisting in classifying the input image into one of the learned categories. The pictures organized by the chip in the study were hand-drawn, letter-like characters.
Continue reading | Check out the paper and post
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Today, customers can raise support tickets through multiple channels like – web, mobile, chat-bots, emails, or phone calls. When a support ticket is raised by a customer, it is processed and assigned to a category based on the information provided in the ticket. It is then routed to the support group for resolution according to […]
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In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). SageMaker JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions […]
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This is a guest post by Andrew Degenholtz, CEO and Founder of eMagazines, the parent company of ReadAlong.ai. eMagazines’ technology seamlessly transforms print products into premium digital and audio experiences. Leveraging Amazon technology, ReadAlong.ai offers a simple, turn-key way for publishers to add audio to their websites with a single line of code. eMagazines supports […]
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We are excited to announce that in Amazon Forecast, you can now start your forecast horizon at custom starting points, including on Sundays for weekly forecasts. This allows you to more closely align demand planning forecasts to local business practices and operational requirements. Forecast is a fully managed service that uses statistical and machine learning […]
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We’re excited to announce that you can now automatically monitor the accuracy of your Amazon Forecast predictors over time. As new data is provided, Forecast automatically computes predictor accuracy metrics, providing you with more information to decide whether to keep using, retrain, or create new predictors. Monitoring predictor quality and identifying deterioration in accuracy over […]
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Data fuels machine learning (ML); the quality of data has a direct impact on the quality of ML models. Therefore, improving data quality and employing the right feature engineering techniques are critical to creating accurate ML models. ML practitioners often tediously iterate on feature engineering, choice of algorithms, and other aspects of ML in search […]
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https://community.chatwithastrid.com/aprendiendo-espanol-76iwwk5y/post/new-insight-on-how-babies-learn-words-M3dUgc6rrRb83ZD
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In the recent episode of Peter Abbeel's "The Robot Brains" podcast, G.Hinton explains a fascinating hypothesis behind the role of sleep in our lives ("sleep is the process of forgetting negative examples in human contrastive learning framework"). However, he does it in a very general way. Does anybody know where I could read more about that? Academic papers etc.?
Reference: https://youtu.be/2EDP4v-9TUA
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Hi all,
I've been trying to set up DDP multi-node training on AWS for a week and am finally able to make it work.
I didn't find any resources for the same. So thought would write a blog and share it. Please provide feedback and see if this is helpful for you
https://medium.com/@sachinchandra/running-yolo-v5-with-ddp-on-aws-8a4f07a77cf
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👉 They created a pre-trained neural network on the text and finetuned the code to answer mathematics course problems, explain solutions, and produce new questions on a human level. It automatically synthesizes programs and runs them to answer course problems with 81 percent automated accuracy utilizing few-shot learning and OpenAI’s Codex transformer.
👉 They also curated a new dataset of questions from MIT’s most famous mathematics courses. The neural network answers questions from the MATH dataset (including questions on Prealgebra, Algebra, Counting, and Probability, Intermediate Algebra, Number Theory, and Precalculus), which is the current standard of advanced mathematics issues meant to examine mathematical thinking.
Continue reading | Check out the paper and github
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TensorFlow is a Python library for fast numerical computing created and released by Google. It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow. In this post you will discover the TensorFlow library for Deep Learning. […]
The post Introduction to the Python Deep Learning Library TensorFlow appeared first on Machine Learning Mastery.
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Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires orchestrating a cluster of GPUs to perform a single synchronized calculation. As cluster and model sizes have grown, machine learning practitioners have developed an increasing
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It’s no secret that writing a thank you letter can be difficult. You want to express your gratitude, but you also don’t want to sound too…
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The Problem
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Some may call this GFN Thursday legendary as Mass Effect Legendary Edition and It Takes Two join the GeForce NOW library. Both games expand the available number of Electronic Arts games streaming from our GeForce cloud servers, and are part of 10 new additions this week. Adventure Awaits In The Cloud Relive the saga of Read article >
The post Out of This World: ‘Mass Effect Legendary Edition’ and ‘It Takes Two’ Lead GFN Thursday Updates appeared first on NVIDIA Blog.
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https://www.reddit.com/r/MachineLearning/comments/sfbtds/p_webtoonme_project_selfie_to_webtoon_style/?utm_source=share&utm_medium=web2x&context=3
project page: https://github.com/webtoon/WebtoonMe
demo page: https://webtoon.github.io/WebtoonMe/app.html
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Training always takes too long. If it takes an hour, it would be better if it took 30 minutes, or maybe 15 minutes... or just 1 minute, why not? And if you want to speed up training, the techs available usually require to increase the complexity of the training process, whether it's making trade-off in terms of accuracy or time for the developer to learn a new framework. Often times it's trial and error, playing with parameters, training recipes, or switching framework/model. That's definitely not ideal.
“Fast & easy-to-use” These were keywords that motivated me to work on a new way of doing training, the library nebulgym, which now is open-source (github link).
Fast
Training should be fast, period. Wouldn't it be great if in the near future you could train a GPT3 from scratch on your l…
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I found the generalization problems of machine learning, especially in deep learning, very attractive, I wonder what are some attractive problems nowadays.
I know about the double descent problem, which I believe is quite interesting, and does not have a valid answer at this moment.
I also know about the implicit inductive bias introduced by SGD, but it seems has been studied widely recently especially with the tool of NTK.
I wonder what are some other interesting phenomenon like these mysteries?
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Hey there!
We just published the third Unit of Deep Reinforcement Learning Class 🥳. In this Unit, you'll learn about Deep Q-Learning and train a DQN agent to play Atari games using RL-Baselines3-Zoo.
You’ll be able to compare the results of your Q-Learning agent using the leaderboard
The Deep Q-Learning chapter 👉 https://huggingface.co/blog/deep-rl-dqn
The hands-on 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit3/unit3.ipynb
The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
https://i.redd.it/mq8fqnmkxe491.gif
Deep RL Class, is a free course from beginner to expert, self-paced where you’ll get solid foundations of Deep Reinforcement Learning in theory and practice with hands-on using famous RL libraries such SB3, RL-Baselines3-Zoo, RLlib, CleanRL…
You can sign up here 👉 http://eepurl.com/h1pElX
And if you have questions and feedback I would love to answer them.
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👉 LinkBERT consists of three steps:
(1) obtaining links between documents to build a document graph from the text corpus,
(2) creating link-aware training instances from the graph by placing linked documents together, and finally
(3) pretraining the LM with link-aware self-supervised tasks: masked language modeling (MLM) and document relation prediction (DRP).
👉 LinkBERT is especially effective for multi-hop reasoning and few-shot QA (+5% absolute improvement on HotpotQA and TriviaQA)
Continue reading | Check out the paper, github and blog post
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Training always takes too long. If it takes an hour, it would be better if it took 30 minutes, or maybe 15 minutes... or just 1 minute, why not? And if you want to speed up training, the techs available usually require to increase the complexity of the training process, whether it's making trade-off in terms of accuracy or time for the developer to learn a new framework. Often times it's trial and error, playing with parameters, training recipes, or switching framework/model. That's definitely not ideal.
“Fast & easy-to-use” These were keywords that motivated me to work on a new way of doing training, the library nebulgym, which now is open-source (github link).
Fast
Training should be fast, period. Wouldn't it be great if in the near future you could train a GPT3 from scratch on your l…
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min )
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In today’s digital landscape, customers are expecting a high-quality experience that is responsive and delightful. Chatbots and virtual assistants have transformed the customer experience from a point-and-click or a drag-and-drop experience to one that is driven by voice or text. You can create a more engaging experience by further augmenting the interaction with a visual […]
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Data scientists and machine learning (ML) engineers often prepare their data before building ML models. Data preparation typically includes data preprocessing and feature engineering. You preprocess data by transforming data into the right shape and quality for training, and you engineer features by selecting, transforming, and creating variables when building a predictive model. Amazon SageMaker […]
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NVIDIA GPUs will play a key role interpreting data streaming in from the James Webb Space Telescope, with NASA preparing to release next month the first full-color images from the $10 billion scientific instrument. The telescope’s iconic array of 18 interlocking hexagonal mirrors, which span a total of 21 feet 4 inches, will be able Read article >
The post Stunning Insights from James Webb Space Telescope Are Coming, Thanks to GPU-Powered Deep Learning appeared first on NVIDIA Blog.
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Do folks here have good references for a summary in what progress has been made in neural tangent kernel (NTK) research? There's an excellent and approachable blog post about the state of the field in 2018-2019 (https://rajatvd.github.io/NTK/), but I assume that there's been a lot of follow-on work since. Thanks!
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As a globally-used system with millions of developers employing it as their primary building environment, Kubernetes is one of the most well-known tools for container management in the world.
The post The Four Golden Signals of Kubernetes appeared first on Data Science Central.
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MIT professor will leverage his research into machine learning and computer science, as well as his role as a practicing cardiologist, toward educating clinician-scientists and engineers.
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In this post, we talk about how to split a machine learning (ML) dataset into train, test, and validation datasets with Amazon SageMaker Data Wrangler so you can easily split your datasets with minimal to no code. Data used for ML is typically split into the following datasets: Training – Used to train an algorithm […]
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This is a guest post co-written by Juan Francisco Fernandez, ML Engineer in Adevinta Spain, and AWS AI/ML Specialist Solutions Architects Antonio Rodriguez and João Moura. InfoJobs, a subsidiary company of the Adevinta group, provides the perfect match between candidates looking for their next job position and employers looking for the best hire for the […]
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Dionysios Satikidis was playing FIFA 19 when he realized the simulated soccer game’s realism offered a glimpse into the future for training robots. An expert in AI and autonomous systems at Festo, a German industrial control and automation company, he believed the worlds of gaming and robotics would intersect. “I’ve always been passionate about technology Read article >
The post Festo Develops With Isaac Sim to Drive Its Industrial Automation appeared first on NVIDIA Blog.
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This week In the NVIDIA Studio takes off with the debut of Top Goose, a short animation created with Omniverse Machinima and inspired by one of the greatest fictional pilots to ever grace the big screen. The project was powered by PCs using the same breed of GPU that has produced every Best Visual Effects nominee at the Academy Awards for 14 years: multiple systems with NVIDIA RTX A6000 GPUs and an NVIDIA Studio laptop — the Razer Blade 15 with a GeForce RTX 3070 Laptop GPU.
The post Feel the Need … for Speed as ‘Top Goose’ Debuts In the NVIDIA Studio appeared first on NVIDIA Blog.
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An AI wrote this article. The last words are very frightening!
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The expressivity of current deep probabilistic models can be improved by selectively prioritizing statistical dependencies between latent variables that are potentially distant from each other. Attention mechanisms can be leveraged to build more expressive variational distributions in deep probabilistic models by explicitly modeling both nearby and distant interactions in the latent space. Attentive inference reduces computational footprint by alleviating the need for deep hierarchies.
👉 It achieves state-of-the-art log-likelihoods while using fewer latent layers and requiring less training time than existing models. The proposed holistic inference reduces computational footprint by alleviating the need for deep hierarchies.
Continue reading | Check out the paper, github and blog post
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Hi everyone! We've recently introduced several new features to Graphsignal Profiler that I'm very excited to share here.
In addition to TensorFlow, PyTorch and Keras, the profiler now natively supports Hugging Face and PyTorch Lightning.
A built in support for distributed training has been added. More info here.
Trace information (using Chrome trace format) is now automatically available in the profiles.
To try it out, simply follow the Quick Start guide. Any feedback is welcome!
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Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. The first option is fast start, collaborative notebooks accessible within Amazon SageMaker Studio – a fully integrated development environment (IDE) for machine learning. You can quickly launch notebooks in Studio, easily dial up or […]
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Retail businesses are data-driven—they analyze data to get insights about consumer behavior, understand shopping trends, make product recommendations, optimize websites, plan for inventory, and forecast sales. A common approach for sales forecasting is to use historical sales data to predict future demand. Forecasting future demand is critical for planning and impacts inventory, logistics, and even […]
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A big problem with the “Data Strategy” conversation is that many organizations think of a “Data Strategy” as a deliverable, not a journey. A Data Strategy, like a Business Strategy, should ebb and flow depending upon what is “valuable” to the organization given the current business environment. And the current business environment is constantly changing. … Read More »Building Value-driven Data Strategy and Economies of Learning – Part 1
The post Building Value-driven Data Strategy and Economies of Learning – Part 1 appeared first on Data Science Central.
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Let’s be honest: The way we’ve been managing data for the past 30 years hasn’t fundamentally changed. Yes, the shift to the cloud and the Modern Data Stack is making the life of data engineers easier because you don’t have to worry as much about infrastructure. Want data in a warehouse? Click, click, click… You… Read More »The Data Product ABCs – A Framework for Bringing Product Thinking to Data
The post The Data Product ABCs – A Framework for Bringing Product Thinking to Data appeared first on Data Science Central.
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Implementation Repo: https://github.com/adityabingi/Dreamer
This work is a reproduction and comparison of Dreamerv1 and v2 for continuous control tasks of the dm_control suite. Training of both algorithms is done entirely on single free GPUs of google colab for 100k timesteps due to colab's strict timeouts. Performance comparison plots across 5 continuous control tasks from dm_control can be found in the repo
Hope this is useful for those trying to reproduce these algorithms
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Computer vision specialist Landing AI has a unique calling card: Its co-founder and CEO is a tech rock star. At Google Brain, Andrew Ng became famous for showing how deep learning could recognize cats in a sea of images with uncanny speed and accuracy. Later, he founded Coursera, where his machine learning courses have attracted Read article >
The post Vision in the Making: Andrew Ng’s Startup Automates Factory Inspection appeared first on NVIDIA Blog.
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Artificial intelligence is one of the most talked-about technologies today. And for good reason — it has the potential to change the world…
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Vendors, consultants, and their clients have been talking in data fabric terms for close to a decade now, if not longer. If “big data” was the problem to solve, then a data fabric suggested a ready solution. John Mashey, then chief scientist at Silicon Graphics, used the term “big data” to describe the wave of… Read More »Comparing data fabrics, data meshes and knowledge graphs
The post Comparing data fabrics, data meshes and knowledge graphs appeared first on Data Science Central.
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The capacity of large transformer-based language models to do few-shot learning is intriguing. These models can be generalized from a few samples of a new topic that they haven’t been trained on before. Previous research in the field of meta-learning has shown how neural networks can execute few-shot learning from a few examples without the requirement for weight updates – this is also known as in-context learning because the output is conditioned on the context.
To do this, the Deepmind researchers created a training program that specifically encourages in-context learning, a technique known as meta-training. The capacity for in-context learning in transformer language models, on the other hand, is emergent. Few-shot learning isn’t directly addressed in the model’s transformer architecture or learning aim.
The discovery that many natural data sources, including natural language, deviate from normally supervised datasets due to a few significant traits inspired this idea. Natural data, for example, is ‘bursty’ in terms of time. That is, rather than tending to appear in clusters, a given entity (word, person, item, etc.) may have a distribution that is not uniform across time.
These results give insight into why FSL is seen in large language models, and how we might achieve emergent FSL in other domains!
Continue reading | Check out the paper
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Salesforce AI Research has proposed a new video-and-language representation learning framework called ALPRO. This framework can be used for pre-training models to achieve state-of-the-art performance on tasks such as video-text retrieval and question answering.
ALPRO follows the “pre-training-then-fine-tuning” paradigm utilized in the VLP techniques described previously but overcomes their drawbacks. The approach runs on poorly sampled video frames and achieves more efficient cross-modal alignment without explicit object detectors.
The ultimate objective of the novel strategy is to enhance the performance of subsequent tasks, such as video-text retrieval and video question answering (video QA). As proposed in ALPRO, enhanced pre-training technique results in enhanced video-language representations, contributing to enhanced performance on subsequent tasks.
Continue reading | Check out the paper and github
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After 10 years and nearly 5 million enrollments, Stanford will be closing new enrollments for the Machine Learning course on Coursera from June 14, 2022. It will be replaced by a more in-depth Machine Learning Specialization by Stanford Online and Deeplearning.ai and will be available in June.
The most iconic MOOC to ever exist?
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This paper argues that the uninterpreability of deep neural networks need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs.
https://arxiv.org/abs/2206.00520
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https://youtu.be/efPrtcLdcdM
GPT-4chan was trained on over 3 years of posts from 4chan's "politically incorrect" (/pol/) board.
Website (try the model here): https://gpt-4chan.com
Model: https://huggingface.co/ykilcher/gpt-4chan
Code: https://github.com/yk/gpt-4chan-public
Dataset: https://zenodo.org/record/3606810#.YpjGgexByDU
OUTLINE:
0:00 - Intro
0:30 - Disclaimers
1:20 - Elon, Twitter, and the Seychelles
4:10 - How I trained a language model on 4chan posts
6:30 - How good is this model?
8:55 - Building a 4chan bot
11:00 - Something strange is happening
13:20 - How the bot got unmasked
15:15 - Here we go again
18:00 - Final thoughts
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Hello all,
I'm trying to get a system going for a GNN where an agent will move along a network of nodes and edges. Each state the agent has traveled to a new node, and the total distance traveled goes up. My problem is in getting the network loaded in from a networkx graph.
Heres some code that reproduces the error:
from torch_geometric.utils import from_networkx import networkx as nx nodes = [ (0, {'y': 37.3348363, 'x': -121.888113}), (1, {'y': 37.3353111, 'x': -121.887118}), (2, {'y': 37.3358288, 'x': -121.8860567}), ] edges = [ (0, 1, {'osmid': 358475012, 'oneway': False, 'highway': 'residential', 'length': 72.482, 'geometry': '', 'speed_kph': 25.0, 'bearing': 149.1}), (0, 2, {'osmid': [416909272, 680787590], 'oneway': False…
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Many applications meant for industrial equipment maintenance, trade monitoring, fleet management, and route optimization are built using open-source Cassandra APIs and drivers to process data at high speeds and low latency. Managing Cassandra tables yourself can be time consuming and expensive. Amazon Keyspaces (for Apache Cassandra) lets you set up, secure, and scale Cassandra tables […]
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Organizational diversity, equity and inclusion (DEI) initiatives are at the forefront of companies across the globe. By constructing inclusive spaces with individuals from diverse backgrounds and experiences, businesses can better represent our mutual societal needs and deliver on objectives. In the article How Diversity Can Drive Innovation, Harvard Business Review states that companies that focus […]
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Amazon SageMaker Serverless Inference is an inference option that enables you to easily deploy machine learning (ML) models for inference without having to configure or manage the underlying infrastructure. SageMaker Serverless Inference is ideal for applications with intermittent or unpredictable traffic. In this post, you’ll see how to use SageMaker Serverless Inference to reduce cost when […]
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Many companies must tackle the difficult use case of building a highly optimized recommender system. The challenge comes from processing large volumes of data to train and tune the model daily with new data and then make predictions based on user behavior during an active engagement. In this post, we show you how to use […]
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You may have applications that generate streaming data that is full of records containing customer case notes, product reviews, and social media messages, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing positive or negative sentiment, translate their comments into a common language, and create […]
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AI Weirdness: the strange side of machine learning
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Today, we’re excited to announce that Amazon SageMaker now supports the ability to configure Instance Metadata Service Version 2 (IMDSv2) for Notebook Instances, and for administrators to control the minimum version with which end-users create new Notebook Instances. You can now choose IMDSv2 only for your new and existing SageMaker Notebook Instances to take advantage […]
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Amazon Kendra offers highly accurate semantic and natural language search powered by machine learning (ML). Many organizations use GitHub as a code hosting platform for version control and to redefine collaboration of open-source software projects. A GitHub account repository might include many content types, such as files, issues, issue comments, issue comment attachments, pull requests, […]
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Financial documents such as bank, loan, or mortgage statements are often formatted to be visually appealing and easy to read for the human eye. These same features can also make automated processing challenging at times. For instance, in the following sample statement, merging rows or columns in a table helps reduce information redundancy, but it […]
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Fraud plagues many online businesses and costs them billions of dollars each year. Financial fraud, counterfeit reviews, bot attacks, account takeovers, and spam are all examples of online fraud and malicious behaviors. Although many businesses take approaches to combat online fraud, these existing approaches can have severe limitations. First, many existing methods aren’t sophisticated or […]
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This is published in Nature, so supposedly more notable than yet another multimodal experiment. But the way the article presents the results, leaves me confused about how this compares and contrasts to e.g. DeepMind Gato?
https://www.nature.com/articles/s41467-022-30761-2
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Hello everyone!! It's been a while!! Years back I released Hyperlearn https://github.com/danielhanchen/hyperlearn. It has 1.2K Github stars, where I made tonnes of algos faster.
I was a bit busy back at NVIDIA and my startup, and I've been casually developing some algos. The question is are people still interested in fast algorithms? Does anyone want to collaborate on reviving Hyperlearn? (Or making a NEW package?) Note the current package is ahhh A MESSS... I'm fixing it - sit tight!!
NEW algos for release:
PCA with 50% less memory usage with ZERO data corruption!! (Maths tricks :)) (ie no need to do X - X.mean()!!!)) How you may ask???!
Randomized PCA with 50% less memory usage (ie no need to do X - X.mean()).
Linear Regression is EVEN faster with now Pivoted Cholesky making algo …
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Some context can be provided in the prompt, but for the bigger picture it is insufficient. I understand companies will not release anything like it until they solve the bias/censorship issues somehow, but did anyone mention an internal demo, or a project in progress? Or is there a lower scale open source experimental project?
It would be so much fun to get some summaries or Q&A on the current events, latest science/tech developments, etc.
Edit (thanks u/adt): There are projects trying to connect a language model to Internet and/or some add-on memory for facts. For example, WebGPT (which might be on its way to a product launch), BlenderBot 2.0 by Meta, and Jurassic-X by AI21.
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Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models. Computers that can read and write are here, and they have the potential to fundamentally impact daily life. The future of human–machine interaction is full
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On Techno-Prophets in The Service of Humanity
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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Celebrate the onset of summer this GFN Thursday with 25 more games joining the GeForce NOW library, including seven additions this week. Because why would you ever go outside? Looking to spend the summer months in Space Marine armor? Games Workshop is kicking off its Warhammer Skulls event for its sixth year, with great discounts Read article >
The post GFN Thursday Jumps Into June With 25 New Games Coming This Month appeared first on NVIDIA Blog.
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Paper: https://arxiv.org/abs/2205.15967
Website: https://sites.google.com/view/esper-paper
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I'm trying to build a simple pygame renderer following the guidelines at https://www.gymlibrary.ml/content/environment_creation/#rendering however the function Renderer is not available from gym.utils.renderer. I have installed gym version 0.23.1.
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Meta Researchers introduce a new embodied AI platform called ‘MyoSuite’ that combines motor and neural intelligence to solve biomechanical control problems using machine learning (ML). To meet the data requirements of modern machine learning (ML) algorithms, MyoSuite’s muscle models are up to 4,000 times faster than other simulators.
Since physiologically realistic movements such as twirling a pen or manipulating Baoding balls can be generated, this research could significantly impact areas such as the development of prosthetics and post-injury rehabilitation.
In the metaverse, these models will aid in creating avatars that move more realistically, making the experience more expressive and immersive.
Continue reading | Check out the paper, Github, blog and project
https://i.redd.it/srxyakklgy291.gif
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Pretraining for rapid adaptation to new games has not been explored widely on Atari games despite being a natural and well-motivated task due to its relevance to how humans transfer knowledge to new games.
Pretraining with the DT objective performs the best across all games. All methods with pretraining outperform training CQL from scratch, which verifies our hypothesis that pretraining on other games should indeed help with rapid learning of a new game.
https://i.imgur.com/lY2DH4i.png
Multi-Game Decision Transformers
https://sites.google.com/view/multi-game-transformers
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Thousands of children participate in MIT-developed artificial intelligence curriculum.
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Having an environment capable of delivering Amazon SageMaker notebook instances quickly allows data scientists and business analysts to efficiently respond to organizational needs. Data is the lifeblood of an organization, and analyzing that data efficiently provides useful insights for businesses. A common issue that organizations encounter is creating an automated pattern that enables development teams […]
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In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that […]
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Make the best trade-offs and optimise model speed with TurinTech AI
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If you’re a manager tasked with overseeing a return to the office strategy, the last year has likely been a headache for you. In the Summer of 2021, many companies had begun calling their workers back into the office, by the following Fall, the Omicron variant of Covid-19 was beginning to ramp up and, just… Read More »DSC Weekly 31 May 2020: Why Is Returning To the Office So Hard?
The post DSC Weekly 31 May 2020: Why Is Returning To the Office So Hard? appeared first on Data Science Central.
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https://arxiv.org/abs/2205.14807
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Amazon Lookout for Metrics is an AWS service that uses machine learning (ML) to automatically monitor the metrics that are most important to businesses with greater speed and accuracy. The service also makes it easier to diagnose the root cause of anomalies, such as unexpected dips in revenue, high rates of abandoned shopping carts, spikes […]
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The home-buying process can feel like an obstacle course — finding the perfect place, putting together an offer and, the biggest hurdle of all, securing a mortgage. San Francisco-based real-estate technology company Doma is helping prospective homeowners clear that hurdle more quickly with the support of AI. Its machine learning models accelerate properties through the Read article >
The post The Closer: Machine Learning Helps Banks, Buyers Finalize Real Estate Transactions appeared first on NVIDIA Blog.
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The year of the tiger comes into focus this week In the NVIDIA Studio, which welcomes 3D creature artist Massimo Righi. An award-winning 3D artist with two decades of experience in the film industry, Righi has received multiple artist-of-the-month accolades and features in top creative publications.
The post Fantastical 3D Creatures Roar to Life ‘In the NVIDIA Studio’ With Artist Massimo Righi appeared first on NVIDIA Blog.
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Identifying causal effects is an integral part of scientific inquiry. It helps us understand everything from educational outcomes to the effects of social policies to risk factors for diseases. Questions of cause-and-effect are also critical for the design and data-driven evaluation of many technological systems we build today. To help data scientists better understand and […]
The post DoWhy evolves to independent PyWhy model to help causal inference grow appeared first on Microsoft Research.
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“Clones are organisms that are exact genetic copies of any living organism. Every single bit of their DNA is identical.”
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Today, Layer goes open-source to make machine learning more accessible and contribute to ML's growth and evolution.
Machine Learning is becoming the default way to build technology. It's how you make your apps smarter, your systems more reliable and your businesses smarter. This is mostly possible by the open-science efforts; from open-source ML frameworks to open datasets.
We will open-source more including our roadmap. Meanwhile, check out our repo, and don't forget to give us a star!
https://github.com/layerai/sdk
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Last week, the 80s pop group ABBA performed a ‘hologram concert’ based on what they called as ‘ABBAtars’ By all measures in the media, it was very successful From a technological perspective, could it offer a ‘killer app’ for 5G and the Metaverse? Firstly, a hologram concert is not a hologram as we know it… Read More »Could ABBAtars be the business model for the metaverse and 5G?
The post Could ABBAtars be the business model for the metaverse and 5G? appeared first on Data Science Central.
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To reap all the benefits of cloud computing technology, it’s important to secure the cloud during and after migration.
The post How to Protect Your Cloud from Cyberattacks During and After Migration appeared first on Data Science Central.
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Ecommerce is a cutthroat industry, and it’s only getting more competitive.
The post How 3 Key Ecommerce Metrics Can Inform Your Data Analysis appeared first on Data Science Central.
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Researchers grappling with today’s grand challenges are getting traction with accelerated computing, as showcased at ISC, Europe’s annual gathering of supercomputing experts. Some are building digital twins to simulate new energy sources. Some use AI+HPC to peer deep into the human brain. Others are taking HPC to the edge with highly sensitive instruments or accelerating Read article >
The post NVIDIA Accelerates AI, Digital Twins, Quantum Computing and Edge HPC at ISC 2022 appeared first on NVIDIA Blog.
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Jorge Cardoso wears many hats, and that’s appropriate given he has so many brains. A hundred thousand of them to be exact. Cardoso is a teacher, a CTO, an entrepreneur, a founding member of the MONAI open source consortium and a researcher in AI for medical imaging. In that last role, Cardoso and his team Read article >
The post The Man With 100,000 Brains: AI’s Big Donation to Science appeared first on NVIDIA Blog.
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It’s time to start building tomorrow’s hybrid quantum computers. The motivation is compelling, the path is clear and key components for the job are available today. Quantum computing has the potential to bust through some of today’s toughest challenges, advancing everything from drug discovery to weather forecasting. In short, quantum computing will play a huge Read article >
The post The Road to the Hybrid Quantum-HPC Data Center Starts Here appeared first on NVIDIA Blog.
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As global climate change accelerates, finding and securing clean energy is a crucial challenge for many researchers, organizations and governments. The U.K.’s Atomic Energy Authority (UKAEA), through an evaluation project at the University of Manchester, has been testing the NVIDIA Omniverse simulation platform to accelerate the design and development of a full-scale fusion powerplant that Read article >
The post Scientists Building Digital Twins in NVIDIA Omniverse to Accelerate Clean Energy Research appeared first on NVIDIA Blog.
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Across Europe and the U.S., HPC developers are supercharging supercomputers with the power of Arm cores and accelerators inside NVIDIA BlueField-2 DPUs. At Los Alamos National Laboratory (LANL) that work is one part of a broad, multiyear collaboration with NVIDIA that targets 30x speedups in computational multi-physics applications. LANL researchers foresee significant performance gains using Read article >
The post HPC Researchers Seed the Future of In-Network Computing With NVIDIA BlueField DPUs appeared first on NVIDIA Blog.
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Highly accurate digital representations of physical objects or systems, or “digital twins,” will enable the next era of industrial virtualization and AI, executives from NVIDIA and BMW said Tuesday. Kicking off the ISC 2022 conference in Hamburg, Germany, NVIDIA’s Rev Lebaredian (left), vice president for Omniverse and simulation technology, was joined by Michele Melchiorre, senior Read article >
The post Hyperscale Digital Twins to Give Us “Amazing Superpowers,” NVIDIA Exec Says at ISC 2022 appeared first on NVIDIA Blog.
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The design and position of breadcrumb navigation on a webpage is typical and has become an established practice for a long time. However, as the world shifts to a mobile-first web environment, many website designers are getting it wrong or forgetting to include it in their navigation. Doing this can be a blunder because it… Read More »Best Practices for Implementing Breadcrumb SEO Strategy for Mobiles
The post Best Practices for Implementing Breadcrumb SEO Strategy for Mobiles appeared first on Data Science Central.
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https://www.kaggle.com/competitions/amex-default-prediction/overview
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AI Weirdness: the strange side of machine learning
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A nice video by MKBHD that gives some really nice insights on the effect that Dalle 2 or ml will have in the future.
Considering that the model can make multiple examples in a very short space of time, things are getting very interesting and scary l will say.
https://youtu.be/MwAAH9tBoMg
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Article and a source code:
https://dmitryelj.medium.com/solving-sudoku-in-real-time-using-a-convolutional-neural-network-and-opencv-e47a92478dce
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I have a question about SimCLR that I have not been able to understand.
In the numerator of the SimCLR loss function, $z_i$ is the original image, and $z_j$ is the augmented version of $z_i$. We want the distance to those to be small.
Similarly in the denominator, $z_k$ for K = 1:2N, k =/= i, is the index of all other images in the batch. Those are going to be pushed away from $z_i$.
This is fine, but what guarantee do we have that k wont belong to an image of the same class as image i?
The way this is structured, we will also end up pushing away images of the same class.
Thanks
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In deep learning, there are different training methods. Which one we use in an AI project depends on the data provided by our customer: how…
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Can machines experience emotions? They might, according to Hume AI, an AI research lab and technology company that aims to “ensure artificial intelligence is built to serve human goals and emotional well-being.” So how can AI genuinely understand how we are feeling, and respond appropriately? On this episode of NVIDIA’s AI Podcast, host Noah Kravitz Read article >
The post A Devotion to Emotion: Hume AI’s Alan Cowen on the Intersection of AI and Empathy appeared first on NVIDIA Blog.
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It’s a beautiful day to play video games. And it’s GFN Thursday, which means we’ve got those games. Ten total titles join the GeForce NOW library of over 1,300 games, starting with the release of Roller Champions – a speedy, free-to-play roller skating title launching with competitive season 0. Rollin’ Into the Weekend Roll with Read article >
The post Ready, Set, Game: GFN Thursday Brings 10 New Titles to GeForce NOW appeared first on NVIDIA Blog.
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Recalling the French linguist who deciphered the Rosetta Stone 150 years ago, Hewlett Packard Enterprise today switched on a tool to unravel its customers’ knottiest problems. The Champollion AI supercomputer takes its name from Jean-François Champollion (1790-1832), who decoded hieroglyphics that opened a door to study of ancient Egypt’s culture. Like Champollion, the mega-system resides Read article >
The post Deciphering the Future: HPE Switches on AI Supercomputer in France appeared first on NVIDIA Blog.
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Today, Packaging is not just about covering a product for a better sale. Interactive packaging is a new trend in the packaging industry which mainly focuses on customer satisfaction and engagement. BLE codes, AI, and IoT are key technologies in interactive packaging which is helping users with the product attributes and user instructions. Also, Incorporation… Read More »Interactive Packaging: How to Make Packaging Smarter with AI and IoT
The post Interactive Packaging: How to Make Packaging Smarter with AI and IoT appeared first on Data Science Central.
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First Medium article is out!
Come see how Optumi is thinking about the shifting workflow needs of data science and machine learning professionals.
https://medium.com/@optumi/scale-ml-experiments-from-jupyterlab-to-the-cloud-141bd645d8e9
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If you are interested in getting your text converted to an image by Google Brain Imagen use the following link:
https://twitter.com/mo_norouzi/status/1529497457234780162?s=20&t=3K_M972bMeGRR2wG6kobHQ
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This blog post (Optimizing TensorFlow Lite Runtime Memory) says that TensorFlow Lite employs different approaches to handle intermediate tensors which occupy large amounts of memory. Is one of them DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training method?
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https://github.com/visualdatabase/fastdup
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A new training approach yields artificial intelligence that adapts to diverse play-styles in a cooperative game, in what could be a win for human-AI teaming.
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Modeling study suggests that the muffled environment in utero primes the brain’s ability to interpret some types of sound.
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Hi, all,
Glad to share an open source repository PaddleSpeech, which provides SOTA/Streaming ASR witch punctuation, influential TTS with text frontend and a product-ready VPR System.
Code:https://github.com/PaddlePaddle/PaddleSpeech
Features Set:
📦 Ease of Use: low barriers to install. The CLIs are available to quick-start your project.
🔬 Align to the State-of-the-Art: provide high-speed and ultra-lightweight models, and also cutting-edge technology.
🏆 Streaming ASR and TTS System: provide production ready streaming asr and streaming tts system.
💯 Rule-based frontend: the frontend contains Text Normalization and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi).
🛎️ Multi-language: both English and Chinese are supported.
Examples:
Speech Recognition
Input wav: Input.wav
Output text: I knocked at the door on the ancient side of the building.
Text-to-Speech
Input text: Life was like a box of chocolates, you never know what you're gonna get.
Output wav: Output.wav
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https://imagen.research.google/
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In the simple spread env, only position is tracked (https://github.com/openai/multiagent-particle-envs/blob/47e9ee38e605f8a563370b3c7e52a349eca3f6b1/multiagent/scenarios/simple_spread.py#L40)
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Hey guys!
I'm a student and I'm currently working on my dissertation for University. I'm using this as a way of collecting data on the representation of AI in movies and pop culture and I'd appreciate the responses!
Here's the link:
https://forms.gle/1jrzrfuSd3rFD6A17
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Codex is now powering 70 different applications across a variety of use cases through the OpenAI API.
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